Statistical Learning with Sparsity The Lasso and Generalizations |
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| Author:
| Hastie, Trevor
Tibshirani, Robert
Wainwright, Martin |
| Series title: | Chapman and Hall/CRC Monographs on Statistics and Applied Probability Ser. |
| ISBN: | 978-1-4987-1217-0 |
| Publication Date: | May 2015 |
| Publisher: | CRC Press LLC
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| Imprint: | Chapman & Hall/CRC |
| Book Format: | Digital (delivered electronically) |
| List Price: | USD $54.95USD $520.00USD $520.00 |
Book Description:
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In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. The authors cover the lasso for linear regression, generalized penalties, numerical methods for optimization, statistical inference methods for fitted (lasso) models, sparse multivariate analysis,...
More DescriptionIn this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. The authors cover the lasso for linear regression, generalized penalties, numerical methods for optimization, statistical inference methods for fitted (lasso) models, sparse multivariate analysis, graphical models, compressed sensing, and much more.